基于Gaze的共享自主框架与实时动作原始识别机器人操纵者的机器人操纵者
概括
机器人可以更好地预测人类的意图使用基于眼睛的目光的新行动预测 (AP) 系统. 这种先进的人机协作方法提供了更无的机器人运动和用户偏好,而不是传统的凝视触发 (GT).
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 机器人需要实时识别人类意图,以便在日常生活任务中有效协作.
- 眼睛凝视控制提供了一种非侵入性的方法来推断意图并减少操作员的认知负载.
- 传统的凝视触发 (GT) 依赖于持续的凝视来启动预编程的机器人动作.
研究的目的:
- 引入和评估一种新的基于神经网络的动作预测 (AP) 模式,用于从眼睛的目光中推断人类的意图.
- 为了比较AP模式与传统GT模式在人机协作任务中的性能.
- 评估基于视线的控制接口的用户偏好和客观性能指标.
主要方法:
- 开发了一个共享的自主框架,集成3D视线重建和实时意图推断.
- 使用神经网络实现了AP模式,以提取注视特征,用于预测操作员动作原始.
- 进行了用户研究,比较AP和GT模式,使用主观 (利克特尺度) 和客观 (识别延迟) 度量.
主要成果:
- 统计分析表明,与GT模式相比,AP模式促进了更无的机器人运动.
- 参与者普遍表示他们更喜欢AP模式,而不是传统的GT模式.
- 在AP模式中,可以有效地识别和预测操作员预期的动作原始.
结论:
- 拟议的动作预测 (AP) 模式为人机协作提供了视线触发 (GT) 的优质替代方案.
- 使用AP的基于凝视的意图识别可以提高机器人任务性能和用户体验.
- 这项研究促进了直观和高效的人机交互系统的发展.
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